Setup.
For pose estimation on COCO and Crowdpose, we use the standard splits for training and evaluation and report the AP based on OKS as the evaluation metric. We use the standard person detection results from  [1]}.
We follow Simple Baseline [1]}, which upsamples the last feature of the backbone by deconvolutions and predicts the heatmaps at 4\(\times \)  resolution.
The data augmentations include random flipping, half body transformation, random scale, random rotation, grid dropout, and color jittering. The input image size is \(256\times 256\)  by default. We use the AdamW [3]} optimizer with the base learning rate \(5e\) -4 and the weight decay \(5e\) -2. The learning rate is dropped to \(5e\) -5 at the \(120th\)  epoch. We train the models for 150 epochs. We use a layer decay of \(0.9\) /\(0.85\)  for Swin-B/L and the DropPath [4]} of \(0.3\) /\(0.5\)  for Swin-B/L.
